Scene classification method based on nonparametric space judgment hidden Dirichlet model

Abstract

The invention discloses a scene classification method based on a nonparametric space judgment hidden Dirichlet model. The scene classification method mainly overcomes the defect that an existing classification method does not contain scene space information. The scene classification method is implemented in the following steps of (1) inputting images, (2) extracting image block features, (3) initializing nonparametric space judgment hidden Dirichlet model parameters, (4) establishing the nonparametric space judgment hidden Dirichlet model, and (5) classifying image scenes. The scene classification method based on the nonparametric space judgment hidden Dirichlet model utilizes image blocks containing space information, can describe the image scenes more abundantly, and improves the accuracy rate of image scene classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a scene classification method based on a Nonparametric Spatial Discriminative Latent Dirichlet Allocation (NS-DiscLDA) model in the technical field of pattern recognition. The invention can be used for scene classification of natural images and improves the accuracy of scene classification. Background technique [0002] Scene classification is one of the basic tasks of image understanding, and it plays a very important role in scene recognition. Traditional scene classification is usually based on three methods: first, a scene classification method based on image collections based on graph analysis; second, a scene classification method based on supervised manifold learning; third, a scene classification method based on objects and their spatial relationship characteristics method. [0003] The patent "Scene classification method and device based on image collect...

AI Technical Summary

Problems solved by technology

However, the disadvantage of the method of this patent application is that simply using the classifier to classify the image scene lacks the semantic information in the scene, thereby reducing the accuracy of the scene classification

However, the disadvantages of the method of this patent application are: the method uses manifold learning technology, but the classification ability of manifold learning technology is weak, which leads to a decrease in the accuracy of scene classification, and the calculation of this method is complicated If the degree is too high, the speed of scene classification will be reduced

However, the disadvantage of the method of this patent application is that since the method uses the pLSA topic model modeling method, the pLSA topic model lacks prior information, resulting in the loss of detailed information and reducing the accuracy of scene classification

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